Many computing applications, such as computer games, multimedia applications, office applications, or the like, use controls to allow users to manipulate game characters or other aspects of the particular computing application. Typically such controls are based on user input, for example, using controllers, remotes, keyboards, mice, or the like. Unfortunately, such controls may be difficult to learn, thus creating a barrier between a user and such computing applications. Furthermore, such controls may be different than actual game actions or other computing application actions for which the controls are used. Newer technologies for active depth sensing, such as the Kinect™ system from Microsoft® Corporation, have improved three-dimensional reconstruction approaches though the use of structured light (i.e., active stereo) to extract geometry from the video scene as opposed to passive methods, which exclusively rely upon image data captured using video cameras under ambient or natural lighting conditions. Structured light approaches allow denser depth data to be extracted for the generation of free viewpoint video (FVV), since the light pattern provides additional texture on the scene for denser stereo matching. By comparison, passive methods usually fail to produce reliable data at surfaces that appear to lack texture under ambient or natural lighting conditions. Because of the ability to produce denser depth data, active stereo techniques tend to require fewer cameras for high-quality three-dimensional (3D) scene reconstruction.
However, while the use of active stereo techniques has provided for the generation of high-quality 3D scene reconstruction, poor lighting conditions within a scene often prevent the color camera from capturing color images which are sufficient for quality face tracking results. Rather, the detection of such specific details of a scene may be successful only when the scene is properly lit. Moreover, many computing applications may benefit from the extraction of such details from a scene.